AI Lifecycle Management with Handit.AI
Last updated
Last updated
AI model deployment and management often fall short of delivering the expected business outcomes, with up to 80% of AI projects never scaling beyond pilot stages. To address this challenge, Handit.AI provides a comprehensive AI Lifecycle Management Platform that ensures AI models are continuously aligned with business goals and KPIs.
Detect performance degradation, data drift, and emerging issues in real-time, ensuring your models remain accurate, reliable, and aligned with key business objectives.
Handit.AI intercepts HTTP requests (via Axios and Fetch) to automatically track model inputs and outputs, streamlining the tracking process without requiring manual intervention.
The platform easily integrates into your existing AI infrastructure, supporting popular tools such as Amazon SageMaker, TensorFlow, PyTorch, and more. This reduces complexity and accelerates time to value.
Handit.AI continuously evaluates your models' performance and runs A/B testing to compare configurations or versions. This ensures your models deliver the best results by identifying and deploying optimal setups based on performance data.
Real-Time Model Evaluation: Continuously captures metrics like accuracy, latency, and other KPIs.
A/B Testing: Compares multiple model variations, helping you identify the most effective configuration for your specific use case.
Handit.AI provides Automatic Optimization, dynamically fetching the most optimized prompt for your model in real-time using the fetchOptimizedPrompt
function.
How It Works
In the backend, Handit.AI leverages advanced analytics and machine learning to optimize prompts, parameters, and configurations continuously. By calling fetchOptimizedPrompt
, your model will always use the most effective configuration, ensuring peak performance with minimal effort.
Handit.AI addresses the fundamental challenges AI projects face—disconnection between models and business goals, performance degradation, and lack of continuous optimization. By integrating these tools, your AI models become transparent, measurable, and aligned with your business needs, enabling you to:
Improved AI ROI: Directly connect model performance to business outcomes, ensuring that your AI initiatives deliver the value they promise.
Proactive Issue Prevention: Identify performance issues, data drift, and misalignments before they impact results, allowing for rapid optimization.
Reduced Time to Market: Automate much of the manual work required to monitor, evaluate, and optimize AI models, accelerating deployment.
Enhanced Model Performance: Leverage real-time evaluation, A/B testing, and prompt optimization to maximize model efficiency and accuracy.